How to group similar numbers with ranges/conditions and merge IDs using dataframes?

Please, I have a dataframe that is listed in ascending order. My goal is to average similar numbers (numbers that are within 10% of each other in ‘both directions’) and concate their ‘Bell’ name together. For example, the image shows the input and output dataframe. I tried coding it but I stuck on how to progress.

   def full_data_compare(self, df_full = pd.DataFrame()):
       for k in range(df_full): #current rows
           for j in range(df_full): #future rows
               if int(df_full['Size'][k]) - int(df_full['Size'][k])*(1/10) <= int(df_full['Size'][j]) <= int(df_full['Size'][k]) + int(df_full['Size'][k])*(1/10) & int(df_full['Size'][k]) - int(df_full['Size'][k])*(1/10) <= int(df_full['Size'][j]) <= int(df_full['Size'][k]) + int(df_full['Size'][k])*(1/10):

Answer

Assuming you really want to check in both directions that the consecutive values are within 10%, you need to compute two Series with pct_change. Then use it to groupby.agg:

#df = df.sort_values(by='Size') for non-consecutive grouping

m1 = df['Size'].pct_change().abs().gt(0.1)
m2 = df['Size'].pct_change(-1).abs().shift().gt(0.1)

out = (df
 .groupby((m1|m2).cumsum())
 .agg({'Bell': ' '.join, 'Size': 'mean'})
)

NB. If you want to group non-consecutive values, you first need to sort them: sort_values(by='Size')

Output:

          Bell          Size
Size                        
0        A1 A2   1493.500000
1     A1 A2 A3   5191.333333
2     A1 A3 A2  35785.333333
3           A2  45968.000000
4           A1  78486.000000
5           A3  41205.000000